Network Biology: understanding the cells functional organization

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Network Biologyunderstanding the cells
functional organization

• Modified from Slides by Abhishek Rathod

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Biological Terminology

• Protein complex
• Protein Domain
• Homology
• Orthology
• Paralogy

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Graph Terminology
Node Edge Directed/Undirected Degree Shortest
Path/Geodesic distance Neighborhood Subgraph Compl
ete Graph Clique Degree Distribution Hubs
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• Examples of Biological Networks
• Protein-Protein Interaction Networks
• Metabolic Networks
• Signaling Networks
• Transcription Regulatory Networks

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Example of a PPI Network

• Yeast PPI network
• Nodes proteins
• Edges interactions

The color of a node indicates the phenotypic
effect of removing the corresponding protein
(red lethal, green non-lethal, orange slow
growth, yellow unknown).
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A Human PPI Network
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Why is it useful to study PPI networks?

• predict protein function through identification
  of binding partners
• Mechanistic understanding of the gene-function
  phenotype association

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Why is it useful to study structure of PPI
networks?

• Common properties of biological networks
• Can help us relate network structure to
  biological function
• Proteins relative position in a network

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How do we know that proteins interact? (PPI
Identification methods)

• Data
• Yeast 2 hybrid assay
• Mass spectrometry
• Correlated m-RNA expression
• Genetic interactions
• Analysis
• Phylogenetic analysis
• Gene neighbors
• Co-evolution
• Gene clusters
• Also see Comparative assessment of large-scale
  data sets of protein-protein interactions von
  Mering

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PPI Public data sets

• 1.gtThe Munich Information Center for Protein
  Sequences (MIPS)
• http//mips.gsf.de
• 2.gtYeast Proteomics Database (YPD)
• http//www.incyte.com/sequence/proteome/datab
  ases/YPD.html
• 3.gtHuman Reference Protein Database (HRPD)
• http//www.hrpd.org
• 4.gtThe Biomolecular Interaction Network Database
• http//www.binddb.org/
• 5.gtThe General Repository for Interaction
  Datasets (GRID)
• http//biodata.mshri.on.ca/grid/
• 6.gtThe Molecular INTeraction database (MINT)
• mint.bio.uniroma2.it/mint/
• 7.gtOnline Predicted Human Interaction Database
  (OPHID)
• http//ophid.utoronto.ca

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Types of networks 1
A. Social Network Examples the patterns of
friendships between individuals, business
relationships between companies and
intermarriages between families
B. Information
Network Examples Citation Network, World Wide
Web
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Types of Networks 2
C Technological Networks Examples
Electric power grid, network of airline routes,
roads and railways, river networks
D Biological Networks Protein Interaction
Networks, metabolic pathways, gene regulatory
networks, signaling pathways, food web, neural
networks
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Properties of networks

• Small world effect
• Transitivity/ Clustering
• Scale Free Effect
• Maximum degree
• Network Resilience and robustness
• Mixing patterns and assortativity
• Community structure
• Evolutionary origin
• Betweenness centrality of vertices

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Small world effect

• most pairs of vertices in the network seem to be
  connected by a short path

l is mean geodesic distance dij is
the geodesic distance between vertex i and vertex
j l log(N)
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Transitivity/Clustering

• Clustering coefficient C is defined as the
  probability that two neighbors of a given node
  are adjacent.

• Ev is the number of edges between neighbors of
  v.
• A node v has dv neighbors.
• The clustering coefficient C of the whole
  network is the average of Cvs for all nodes v in
  the network.
• An important measure of networks structure is
  the function Ck which is the average clustering
  coefficient of all nodes with k links.

Graph with a big C
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Clustering coefficient
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Network resilience and robustness- ITopological
Robustness

• Effect of removing vertices on shortest path
  length
• For the graph of internet

• The Internet is highly resilient against random
  failure of vertices but highly vulnerable to
  deliberate attack on its highest degree vertices.

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Network resilience and robustness- IIFunctional
and dynamic robustness

• Effect of a perturbation cannot depend on the
  nodes degree only
• Experimentally identified protein complexes tend
  to be composed of uniformly essential or
  non-essential molecules

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Mixing patterns and Assortativity

• In social networks this kind of selective
  linking is called assortative mixing

• Disassortative nature of cellular networks In
  protein interaction networks, highly connected
  nodes (hubs) avoid linking directly to each other
  and instead connect to proteins with only a few
  interactions

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Community structure

• Community structure, is a groups of vertices
  that have a high density of edges within them,
  with a lower density of edges between groups.
• Example Friendship network of children in a
  school
• Citation networks particular areas of research
  interest
• Communities in metabolic networks Functional
  Units
• Hierarchical clustering

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Network Models

• Random Network
• Scale free Network
• Hierarchical Network

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Random Network I

• The ErdösRényi (ER) model of a random network
  starts with N nodes and connects each pair of
  nodes with probability p, which creates a graph
  with approximately pN(N1)/2 randomly placed
  links
• The node degrees follow a Poisson distribution

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Random Network II

• Mean shortest path l log N, which indicates
  that it is characterized by the small-world
  property.
• Random graphs have served as idealized models
  of certain gene networks, ecosystems and the
  spread of infectious diseases and computer
  viruses.

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Scale Free Networks I

• P(k) k ?, where ? is the degree exponent.
• The networks properties are determined by hubs
• The network is often generated by a growth
  process called BarabásiAlbert model

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Scale Free Networks II

• Scale-free networks with degree exponents 2lt?lt3,
  a range that is observed in most biological and
  non-biological networks like the Internet
  backbone, the World Wide Web, metabolic reaction
  network and telephone call graphs.

• The mean shortest path length is proportional to
  log(n)/log(log(n))

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Hierarchical Networks I

• To account for the coexistence of modularity,
  local clustering and scale-free topology in many
  real systems it has to be assumed that clusters
  combine in an iterative manner, generating a
  hierarchical network

• The hierarchical network model seamlessly
  integrates a scale-free
• topology with an inherent modular structure by
  generating a network that has a power-law degree
  distribution with degree exponent ? 1
  ln4/ln3 2.26

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Another hierarchical network
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Hierarchical Networks II

• It has a large system-size independent average
  clustering coefficient ltCgt 0.6. The most
  important signature of hierarchical modularity is
  the scaling of the clustering coefficient, which
  follows C(k) k 1 a straight line of slope 1
  on a loglog plot

• A hierarchical architecture implies that
  sparsely connected nodes are part of highly
  clustered areas, with communication between the
  different highly clustered neighborhoods being
  maintained by a few hubs
• Some examples of hierarchical scale free
  networks.

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Homeworkin case you have not read yet!

• Albert Barabasi et al
• Network Biology understanding the cells
  functional organization
• Jing-Dong et al
• Evidence for dynamically organized modularity in
  the yeast proteinprotein interaction network